- 2851 Views
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Resolved! Training models on big or small clusters
I have a workflow with a model which trains every sunday in Azure Databricks. Sometimes the workflow fails as the max wait time is exceeded (currently I am using 1200 seconds). To solve the problem I was thinking of either increasing the wait time or...
- 2851 Views
- 3 replies
- 5 kudos
- 5 kudos
Hi @Andreas Kaae​ Thank you for posting your question in our community! We are happy to assist you.To help us provide you with the most accurate information, could you please take a moment to review the responses and select the one that best answers ...
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- 1415 Views
- 1 replies
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Cluster list in Microsoft.Azure.Databricks.Client fails because ClusterSource enum does not include MODELS. When you have a model serving cluster, Clu...
Cluster list in Microsoft.Azure.Databricks.Client fails because ClusterSource enum does not include MODELS.When you have a model serving cluster, ClustersApiClient.List method fails to deserialize the API response because that cluster has MODELS as C...
- 1415 Views
- 1 replies
- 3 kudos
- 3 kudos
Cluster list in Microsoft.Azure.Databricks.Client fails because ClusterSource enum does not include MODELS.When you have a model serving cluster, ClustersApiClient.List method fails to deserialize the API response because that cluster has MODELS as C...
- 3 kudos
- 485 Views
- 0 replies
- 0 kudos
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- 1860 Views
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How should I tune hyperparameters when fitting models for every item?
My dataset has an "item" column which groups the rows into many groups. (Think of these groups as items in a store.) I want to fit 1 ML model per group. Should I tune hyperparameters for each group separately? Or should I tune them for the entire...
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For the first question ("which option is better?"), you need to answer that via your understanding of the problem domain.Do you expect similar behavior across the groups (items)?If so, that's a +1 in favor of sharing hyperparameters. And vice versa....
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